KEYWORDS: 3D modeling, Image segmentation, Object recognition, Computing systems, Distance measurement, Detection and tracking algorithms, 3D image processing, Systems modeling, 3D metrology, Cameras
This paper presents an intelligent approach to recognize 3D objects using line structure correspondences. The proposed
approach simultaneous recognizes an object and estimates the pose of the object. In order to achieve this goal, three
challenges should be solved. First of all, line structures that human usually used to describe an object is used to represent
the object. A set of such feature representation that shares the same properties with corresponding model line structures
are first generated from images. Secondly, the structure correspondences are evaluated and ranked by additional features
in the image. Only the most meaningful correspondences are selected. Each correspondence contributes a pose
hypothesis with a transformation matrix. Finally, the approximate model pose hypotheses are estimated and refined
based on the selected correspondences.
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